Development and validation of the nomogram to predict the risk of hospital drug shortages: A prediction model

Abstract

Introduction Reasons for drug shortages are multi-factorial, and patients are greatly injured. So we needed to reduce the frequency and risk of drug shortages in hospitals. At present, the risk of drug shortages in medical institutions rarely used prediction models. To this end, we attempted to proactively predict the risk of drug shortages in hospital drug procurement to make further decisions or implement interventions. Objectives The aim of this study is to establish a nomogram to show the risk of drug shortages. Methods We collated data obtained using the centralized procurement platform of Hebei Province and defined independent and dependent variables to be included in the model. The data were divided into a training set and a validation set according to 7:3. Univariate and multivariate logistic regression were used to determine independent risk factors, and discrimination (using the receiver operating characteristic curve), calibration (Hosmer-Lemeshow test), and decision curve analysis were validated. Results As a result, volume-based procurement, therapeutic class, dosage form, distribution firm, take orders, order date, and unit price were regarded as independent risk factors for drug shortages. In the training (AUC = 0.707) and validation (AUC = 0.688) sets, the nomogram exhibited a sufficient level of discrimination. Conclusions The model can predict the risk of drug shortages in the hospital drug purchase process. The application of this model will help optimize the management of drug shortages in hospitals.

Coping with drug shortages: A study of government-enterprise option cooperation stockpiling strategies for drugs in shortage considering API surrogate stockpiling subsidies

“…Thus, Tucker and Lee suggested that the problem of drug shortages can only be alleviated by improving stakeholder communication and coordination [ 49 , 50 ]. Liu, Shi, and Dong proposed the need to be proactive instead of reactive by designing different prediction models for drug shortage risk and actively carrying out pre-emptive countermeasures for drug shortage [ 51 – 53 ]. Tucker proposed a pharmaceutical supply chain model with improvements in terms of configuration, disruption risk, and recovery speed to reduce the probability of drug shortage [ 54 ].…”